AI’s next frontier is the human cell

Good morning, {{ first_name | AI enthusiasts }}, and welcome to our 4,318 new readers. Math is currently in the thick of its AI moment. Mark Zuckerberg wants biology to be next.

In April, Zuck’s Biohub put $500M into the idea that AI could eventually mimic a living cell. Months later, the pot is up to $1.8B, with the U.S. government, Meta, and Google all hoping that data on billions of cells can give biology a similar moment of its own.

The Rundown: Mark Zuckerberg and Priscilla Chan’s science nonprofit, Biohub, just expanded its $500M Virtual Biology Initiative into a $1.8B effort with the U.S., DeepMind, and Meta, pushing to collect enough data to train AI that can simulate cells.

Why it matters: Fresh off math being solved internally by OpenAI, you might be wondering what field AI comes for next. Zuck and co. are making biology the target, throwing funding, datasets, and talent at Biohub’s moonshot mission to “cure or prevent all disease” — an AI megaproject that’s a lot easier for the public to root for than most.

The Rundown: This live session with Alex Kantrowitz, founder of Big Technology, and Vanta’s SVP of Engineering is where startup builders learn what still matters when AI lets tiny teams build almost anything.

The Rundown: Each week, Rundown AI educator Nate Grahek breaks down what he’s seeing while teaching real teams to use AI. This week, he kicks off a two-part series on slop grenades.

Nate: A friend told me about a colleague whose work she’s always trusted. Then he started tinkering with Copilot, and she got a 15-page report that looked polished and said almost nothing. Decoding it became her job.

Shopify CEO Tobi Lütke says his team has a name for this: slop grenades. Someone pulls the pin, AI writes 15 pages, and the receivers absorb the damage. Lütke’s sharpest question: “Why did we invent decompression and recompression?” BetterUp and Stanford found 40% of U.S. desk workers got one in a single month, costing them about two hours each.

Here’s my hypothesis: the worst slop grenades are made by leaders. Too many blind AI mandates track who logged in and never show anyone what good AI work looks like. When the only standard is “use it,” you get more output.

So leaders need to go first, transparently. Before you ask your team for anything, share one piece of your own AI-assisted work: your process, how you checked it, why it’s useful, and what you’d still question. Label drafts as drafts and ask for critique; Atlassian found that simple step helps reviewers catch the flaws AI polish hides.

When I run this with leadership teams, I start with one rule: it’s never okay to send AI work that takes longer to read and understand than you spent making it. Work worth handing off comes from running the AI operator playbook across multiple chats: gather, brainstorm, draft, critique. Don’t skip the last one. Make AI argue with you before your team has to.

Who on your team is lobbing the most slop grenades, and are you sure it isn’t you?

The Rundown: In this guide, you’ll use Haiku 5.5 on a routine Claude task like sorting customer messages, then ask Sonnet to review the exceptions. You’ll check the sources and compare the full workflow’s cost before using it on real work.

Pro tip: Start with one helper for a small batch rather than one agent per message. Our Pro members get the prepared helper, practice messages, prompts, and checks.

The Rundown: BCG’s 2026 Applied AI Index, based on a survey of more than 1,300 CxOs and senior leaders across 20+ sectors, found that almost 50% of companies now generate value from AI. As agentic AI spreads, these companies have critical lessons for others.

Here’s what survey respondents are saying: